With apologies to Mitchell and Webb.
In a word, my feelings about A.I. — and, in particular, generative A.I. — are complicated. Just search “artificial intelligence” for a reverse chronological back catalogue of where I have landed. It feels like an appropriate position to hold for a set of nascent technologies so sprawling and therefore implying radical change.
Or perhaps that, like so many other promising new technologies, will turn out to be illusory as well. Instead of altering the fundamental fabric of reality, maybe it is used to create better versions of features we have used for decades. This would not necessarily be a bad outcome. I have used this example before, but the evolution of object removal tools in photo editing software is illustrative. There is no longer a need to spend hours cloning part of an image over another area and gently massaging it to look seamless. The more advanced tools we have today allow an experienced photographer to make an image they are happy with in less time, and lower barriers for newer photographers.
A blurry boundary is crossed when an entire result is achieved through automation. There is a recent Drew Gooden video which, even though not everything resonated with me, I enjoyed.1 There is a part in the conclusion which I wanted to highlight because I found it so clarifying (emphasis mine):
[…] There’s so many tools along the way that help you streamline the process of getting from an idea to a finished product. But, at a certain point, if “the tool” is just doing everything for you, you are not an artist. You just described what you wanted to make, and asked a computer to make it for you.
You’re also not learning anything this way. Part of what makes art special is that it’s difficult to make, even with all the tools right in front of you. It takes practice, it takes skill, and every time you do it, you expand on that skill. […] Generative A.I. is only about the end product, but it won’t teach you anything about the process it would take to get there.
This gets at the question of whether A.I. is more often a product or a feature — the answer to which, I think, is both, just not in a way that is equally useful. Gooden shows an X thread in which Jamian Gerard told Luma to convert the “Abbey Road” cover to video. Even though the results are poor, I think it is impressive that a computer can do anything like this. It is a tech demo; a more practical application can be found in something like the smooth slow motion feature in the latest release of Final Cut Pro.
“Generative A.I. is only about the end product” is a great summary of the emphasis we put on satisfying conclusions instead of necessary rote procedure. I cook dinner almost every night. (I recognize this metaphor might not land with everyone due to time constraints, food availability, and physical limitations, but stick with me.) I feel lucky that I enjoy cooking, but there are certainly days when it is a struggle. It would seem more appealing to type a prompt and make a meal appear using the ingredients I have on hand, if that were possible.
But I think I would be worse off if I did. The times I have cooked while already exhausted have increased my capacity for what I can do under pressure, and lowered my self-imposed barriers. These meals have improved my ability to cook more elaborate dishes when I have more time and energy, just as those more complicated meals also make me a better cook.2
These dynamics show up in lots of other forms of functional creative expression. Plenty of writing is not particularly artistic, but the mental muscle exercised by trying to get ideas into legible words is also useful when you are trying to produce works with more personality. This is true for programming, and for visual design, and for coordinating an outfit — any number of things which are sometimes individually expressive, and other times utilitarian.
This boundary only exists in these expressive forms. Nobody, really, mourns the replacement of cheques with instant transfers. We do not get better at paying our bills no matter which form they take. But we do get better at all of the things above by practicing them even when we do not want to, and when we get little creative satisfaction from the result.
It is dismaying to see so many of A.I. product demos show how they can be used to circumvent this entire process. I do not know if that is how they will actually be used. There are plenty of accomplished artists using A.I. to augment their practice, like Sougwen Chen, Anna Ridler, and Rob Sheridan. Writers and programmers are using generative products every day as tools, but they must have some fundamental knowledge to make A.I. work in their favour.
Stock photography is still photography. Stock music is still music, even if nobody’s favourite song is “Inspiring Corporate Advertising Tech Intro Promo Business Infographics Presentation”. (No judgement if that is your jam, though.) A rushed pantry pasta is still nourishment. A jingle for an insurance commercial could be practice for a successful music career. A.I. should just be a tool — something to develop creativity, not to replace it.